**See our complete guide:** [Predictive AI for e-commerce and retail brands](/blog/retail-ecommerce-complete-context-guide).

---

At Faraday, we love when our tools help a mission-driven brand grow in tangible ways, and when that growth could be described as “massive,” we’ll take that too. That’s exactly what happened with [Bee’s Wrap](https://www.beeswrap.com/)—a sustainable alternative to plastic wrap—who built a pitch deck with help from Faraday’s [Personas](https://faraday.ai/docs/how-to/predictions/customer-personas) features which landed them a “massive” (there’s that lovely description in action) placement in over 550 Target locations around the country.

[![Bees wrap product design](/images/blog/bees-wrap.jpg)](/images/blog/bees-wrap.jpg)

## What Bee’s Wrap needed

Bee’s Wrap was preparing for a high-stakes sales pitch to Target, a major national retailer—perhaps you’ve heard of them. The team needed to prove that their customer base was the right kind of shopper that Target wanted to be ‘Target-ing”. For this they needed to show real data that would resonate with a national buyer.

Bee’s Wrap had a major data asset to leverage: their first-party DTC sales data from [Shopify](https://www.shopify.com/). However, this data only tells part of the story. Order data is not going to tell you why someone buys, who they are demographically, or how many more people like them exist across the country. That’s where Faraday came in.

## How Faraday helped

Bee’s Wrap connected their order data to the Faraday platform, and in just one afternoon, they had:

- Detailed demographic and psychographic segments
- Purchase propensities broken down by geography
- A clear, visual representation of who their customers really are—and where to find more of them
- A ZIP-code-level view of where potential was highest—helping them identify which store locations to prioritize in their pitch

This gave Bee’s Wrap a concrete, compelling story to bring into their pitch—one that resonated with Target’s team.

## The results

That pitch didn’t just land—it turned into a deal.

Bee’s Wrap is now rolling out nationwide in over 550 Target stores, starting with an initial wholesale shipment worth well into six figures. It's a massive milestone for the brand—and one they credit, in part, to having the right data in hand at the right moment.

## Why this matters

As [Brendan Taylor](https://www.linkedin.com/in/bttaylor13/), Bee’s Wrap’s CEO, put it:

> “We approached Faraday with a simple but elusive set of questions about our consumers and their demographics. Armed with only our DTC sales data – in one afternoon – Faraday identified crisp consumer segments with actionable demographic and psychographic details. Their easy-to-use interface also provided powerful predictions about propensity to purchase based on geography and other demographic filters."

The Faraday suite can synthesize incredibly powerful insights and predictions out of disparate data sets that previously left marketers and sales leaders scratching their heads. If you have lots of data and want to convert it to knowledge and insights, you’ve come to the right place.”

But here’s the thing, this story isn’t just about one brand—it’s about what happens when companies make their data work for them. With Faraday Personas, even a single afternoon of analysis can unlock game-changing insights.

Whether you’re pitching a retailer, planning a campaign, or just trying to understand your customers better, we’re here to help you tell a data-backed story that lands.

Ready to learn more? [Let’s talk](https://www.linkedin.com/in/davidtoddsmall/).

Bee’s Wrap used Faraday Personas to turn their DTC data into a compelling retail pitch—landing a nationwide deal with over 550 Target stores.

How Bee’s Wrap landed a nationwide retail deal with help from Faraday personas

In our last blog, we explored why ROI analysis at Faraday isn’t just about proving value after the fact—it’s also about continually uncovering new opportunities.

We shared what that looks like at a high level. But this isn’t just a philosophy expressed in our marketing; it’s how we work every day. So today, we’re sharing a story from the \~real world\~ that shows just how much insight you can unlock through our comprehensive ROI evaluation process.

It also drives home an important point: these evaluations can only achieve their full value if you actually engage with them. The top-line numbers might say “Yep, it’s going great,” but just because that’s good news doesn’t mean it reflects the whole story.

Sure, it’s going great, but how great, and where is there room for improvement? Which parts of the campaign are driving the most value? The least value? Do we need to revise any major assumptions to be more accurate?

Indeed when it comes to ROI analyses like these, digging deeper is where the real opportunity lies.

## A deeper dive reveals something unexpected

Hi, ROI extraordinaire Chris Waite here. After I came on full-time at the start of the year as Faraday’s Head of Customer Analytics, I kicked off ROI initiatives with several of our existing customers. The goal was simple: move beyond one-time reports and start treating ROI as an ongoing conversation—one that actively drives value, not just records it.

With one financial services client, we began with my usual process: aligning on key assumptions—value per conversion, attribution logic, unit costs—and setting up monthly check-ins to track progress and dig into results over time.

And the returns looked great. Performance was strong, and the model was holding up. But during one of those check-ins a couple of months ago, someone on their team made an offhand remark that caught our attention: some of their hardest-to-convert prospects—sometimes rejected due to their low predicted conversion rates—actually had the potential to generate the _most_ value if they did convert.

Our interest was piqued, so we ran the numbers.

We analyzed the relationship between Faraday’s prediction scores and the average conversion revenue. Sure enough, there was a clear pattern: while certain low-scoring groups converted less frequently, they delivered more value when they did.

The selection process didn’t need a full overhaul—but it did need a refinement. We brought the results back to the client, and together we incorporated these insights into their approach. By adding an additional variable into our selection process, we could proactively identify the majority of these high-value individuals and ensure their inclusion in the campaign. Once updated, the campaign began to improve—conversion rates dipped slightly (by design), but total revenue went up.

This is a perfect example of how our ROI reporting process uncovers new and unexpected insights that drive value. In this case, we used it to fine-tune suppression logic and reframe scoring thresholds—without a full process overhaul. The outcome was a smarter, more mission-aligned strategy that helped the client prioritize not just likely conversions, but the ones with the greatest potential overall value.

And through that process, we uncovered something more. These high-impact conversions weren’t just good for business—they were cultural wins. Inside the company, signing one of these clients—often individuals carrying significant debt—was seen as a major moment. It represented the core of their mission: to support people who need it most. The whole team felt it.

So yes, ROI is about financial returns. But sometimes, it's also about morale, alignment, and purpose. In this case, their investment in Faraday didn’t just lead to better outcomes on paper—it reinforced the values that drive their business forward.

## From revenue recap to campaign shift

This paradigm shift is exactly Faraday’s goal when we present our clients with our ROI reports. We can talk about them driving value as much as we want but they drive the most value with buy-in and funding from the teams that use them.

In this case, thanks to the deeper ROI dive, the client was able to:

- Identify underutilized customer types
- Refine their lead suppression strategy
- Shift budget allocations across customer segments
- Drive more high-impact conversions

None of that was the original intent. But that’s the point—when ROI becomes an ongoing process, not just a retroactive check-in, it drives value in real time.

## Why Faraday treats ROI like strategy

We’re not sharing this story to pat ourselves on the back—we’re sharing it because it highlights something bigger: most companies treat ROI as a postmortem. We treat it as a feedback loop.

At Faraday, ROI modeling isn’t a final deliverable—it’s built into every engagement from day one. We co-develop success metrics with our clients, revisit assumptions as new information emerges, and treat every report as a living document meant to drive change in real time. Because when ROI is treated as a collaborative, evolving process—not a static snapshot—it doesn’t just validate past performance. It unlocks new value, sharpens ongoing strategy, and turns every insight into an opportunity for future impact.

## Coming up next

In our next post, we’ll pull back the curtain on how we build some of our ROI models at Faraday—what goes into them, what we measure (and why), and how we handle the gray areas most companies avoid.

Because the better you understand your own ROI, the greater your ability to power new growth.

A routine ROI check uncovered a surprising insight that helped a financial services client shift strategy, boost revenue, and realign with their core mission.

This financial services brand’s ROI assessment empowered a whole new strategy

See our complete guide: [Powered by Faraday — embedding predictive AI in your platform](/blog/powered-by-faraday-complete-guide-embedded-predictive-ai)

A leading marketing, technology, and data company that specializes in direct mail campaigns came to Faraday with a problem: their targeting was working, but it wasn’t scalable.

They were helping their clients send mailers based on narrow behavioral triggers from their websites, like cart abandonment or time spent on a product page.

That kind of targeting can be effective—sometimes. But often, the real world is messier.

Maybe someone hops on an apparel site, intent on making a purchase only to get pulled away by their toddler. Maybe a renter clicks through a solar quote tool, strictly because they’re curious, even though they can’t make the final decision to install solar on their roof.

Not all signals mean intent. And more importantly: not every potential converter sends a clear, discernible signal every time they are about to make a purchase.

**Reactive targeting wasn’t enough**

This behavioral-only strategy limited the number of people eligible for mailers—capping both reach and return. By relying solely on website interactions, the company’s campaigns were targeting a very specific kind of customer—one who was already halfway down the funnel and showing overt signals of intent. While this worked for a subset of visitors, it left out a much larger group of people who looked like good fits but didn’t quite meet the behavioral criteria.

The result: capped growth and missed opportunity.

## The solution: smarter lead scoring powered by Faraday

That’s where Faraday came in. By shifting from reactive targeting to predictive lead scoring, the company could stop waiting for the perfect website behavior and start identifying the right people to mail—before they ever clicked “add to cart.”

At the heart of this was the [Faraday Identity Graph](/docs/abstractions/traits)—a rich third-party dataset covering nearly all U.S. consumers and their households. By connecting their first-party behavioral data to this built-in body of rich consumer data points, the company was suddenly able to understand far more about each identified website browser and score them accordingly. But that wasn’t all.

With Faraday’s AI-powered lead scoring, they could now evaluate:

- **Current prospects** not just by their actions—like browsing or abandoning a cart—but by their full profile. Was this someone who matched the traits of past converters? Did their income, lifestyle, or purchasing habits suggest they were ready to buy, even if their behavior didn’t scream it yet?
- And, critically, **potential customers** who had never interacted with the brand at all—opening up entirely new audiences for outreach, no website visit or form fill required.

This opened up a whole new world of targeting. Instead of just reacting to a cart abandonment or a quote request, the company could proactively find high-potential leads who hadn’t yet taken any action—but still looked like strong matches.

No more waiting for someone to raise their hand. Now they could find and prioritize the right people, at the right time—and reach them with personalized mailers before the competition did.

## The result: 3x more mailers, same high conversion rate

Thanks to this predictive scoring approach, the company was able to triple its mailing volume—without compromising performance. That’s because every additional mailer was backed by a validated custom predictive data point: likelihood-to-convert.

Their clients benefited too: more prospects reached, more conversions captured, and better ROI on every campaign.

## Why it worked

Faraday’s AI didn’t replace the company’s behavioral targeting-but rather enhanced it. By layering in predictive signals based on real people’s demographics and past purchasing behavior, the model was able to widen the targeting net without watering the quality of the leads down.

Now, the company helps its clients:

- Expand their reach beyond narrow behavioral triggers
- Identify new high-potential leads using consented, privacy-safe data
- Maintain high conversion rates even as mailing volumes increase

It’s not just smarter targeting—it’s scalable performance.

## The impact

By enriching their behavioral data with Faraday’s Identity Graph and predictive data points, this direct mail platform was able to triple their reach—without compromising on performance. More qualified leads. More efficient targeting. More revenue for everyone involved.

**Ready to scale your outreach without sacrificing ROI?**

Let’s talk about how Faraday can help.

How a direct mail platform used Faraday’s AI to triple their reach with smarter, scalable lead targeting—without losing conversion performance.

Smarter prospecting at scale: How a direct mail provider grew reach without sacrificing performance

**See our complete guide:** [Predictive AI for agencies](/blog/faraday-for-agencies).

---

At Faraday, we’re always looking for ways to make our tools more accessible and valuable for our users. And with our latest update, you can now download [Faraday](https://faraday.ai) [Personas](https://faraday.ai/docs/abstractions/persona_sets) as CSV files directly from the dashboard, bringing even more power to your analysis and reporting processes.

## What changed

There's now a "Download CSV" button on the Persona analysis page and under the Persona set ‘…’ menus. The CSV largely matches the layout of the Persona set analysis page. Although it’s less sortable than our internal Persona report, it’s easier to visually parse and can be used for a much broader range of applications after export.

You’ve always been able to do this in [API](https://faraday.ai/docs/reference/getpersonasets) but this new feature adds this ability to the dashboard—which makes this valuable workflow possible for non-technical marketers to use as well.

![/Graphic representation of FIG categories](/images/blog/downloadcsv.png)

## How this features helps brands and agencies

This update makes it easier than ever for both brands and agencies to leverage Faraday’s in-depth Persona analysis.

**For brands**: this means easier integration of Persona data into your internal reports, or an easier way to communicate the changes in your customers to your executive team.

**For agencies**: this means Persona data can now be integrated into customer reports, BI tools, dashboards for clients, and ultimately can have a bigger impact on the downstream activations and creative choices.

Previously, users had to manually extract Persona information and input it into reports to achieve these ends. This time-consuming process often meant only the most crucial insights were included—but as anyone who uses our Personas knows that they’re most valuable because of the depth of their analysis, so you really want to have access to everything\! Now, with just a click of the "Download CSV" button, you get a clean, visually accessible file that mirrors the layout of our Persona analysis page.

## How EmpowerFi™ is using this new feature to drive results

_This all sounds great in abstract but has anyone actually generated results with this feature?_

Well as it happens, yes\! [EmpowerFi](https://empowerfi.org/)™, a data-driven marketing firm (and one of our favorite partners) is already seeing the tangible benefits of Faraday’s new CSV download feature through their Faraday integration, [IntelliFi](https://empowerfi.org/solutions/intellifi/)™. Erin Lutz, the VP of Operations at EmpowerFi™, shared how this simple update has saved her team a significant amount of time and money.

As an agency that works with multiple clients, including credit unions and banks, Erin historically had to manually extract and input Persona data into reports. This process was time-consuming, often limiting the scope of data they could include. But with the new CSV download feature, Erin can now effortlessly download, customize, and export the information—saving valuable time while delivering more comprehensive insights, directly from Faraday’s dashboard.

This update has not only streamlined Erin’s reporting process but also allowed her to take much greater advantage of the robust Faraday Personas in a range of use cases. As a result, EmpowerFi™ can now offer clients a deeper, data-driven understanding of their target audiences, while advertising Faraday’s powerful Persona tool to their networks.

This feature has made Faraday’s Personas even more accessible, helping EmpowerFi™ continue to deliver exceptional value to their clients.

## Ready to get started\!?

Sounds pretty great, right? We think so too\! If you’re ready to get started today, reach out to me, [Dave Small](https://www.linkedin.com/in/davidtoddsmall/) and I’ll get you set up today\!

Faraday’s new CSV download feature makes it easier for customers to use Persona data outside of our platform—saving time, enhancing analysis, and making insights more accessible for non-technical marketers.

Faraday’s CSV download feature for Personas unlocks new possibilities for agencies like EmpowerFi™

**See our complete guide:** [Predictive AI for e-commerce and retail brands](/blog/retail-ecommerce-complete-context-guide).

---

[Burrow](https://burrow.com) is an innovative furniture brand founded in 2016\. From starting out with thoughtfully designed modular couches to recently expanding their offerings to shelving, coffee tables, and even rugs, they have continuously pushed to understand their customers and cater to their needs.

## The goal

Burrow wanted to personalize its customer journey to drive better conversion rates and more purchases as they expanded their product lines.

## The challenge

Like most companies, Burrow had a cold start problem: they knew a lot about their current customers, but very little about brand new customers or potential customers who had signed up for their newsletter.

## Faraday \+ Iterable

Enter Faraday \+ Iterable. With [Faraday’s Identity Graph](https://faraday.ai/features/faraday-identity-graph), the Burrow team suddenly had access to 1,400+ data points on almost every adult in the US they could combine with their contact data. And with Faraday’s AI tools, the team could identify the patterns and trends of their customers to build segments of leads who looked just like them.

All of these insights then [flow directly to Iterable](https://faraday.ai/docs/integrations/iterable), where their automated journeys enable customer engagement across multiple channels with minimal manual effort. By leveraging automation, informed by Faraday, Burrow delivers timely, relevant content that enhances the customer experience while saving time and resources.

## The results

By partnering with Faraday, Burrow was not only able to know what all of their leads looked like, but which looked like customers who had taken specific actions in the past—whether it was to purchase from a specific category, spend a certain dollar amount, or even prefer a specific color couch over another.

In this way, the Burrow team could engage leads who previously only had provided an email address and deliver highly tailored messaging that matched their consumer profile, with suggested products based on what they were most likely to care about.

All of this messaging was delivered with Iterable, which ensured it was delivered at the right time, and by the right channel for an optimal engagement experience.

With Faraday \+ Iterable, Burrow is building a [predictive customer experience](https://faraday.ai/blog/predictive-customer-experience-is-the-future) that makes it enjoyable for everyone to shop—whether you’re a repeat customer or simply signed up on the newsletter.

Burrow used data and machine learning to power personalization and drive higher conversion rates.

How Burrow uses customer predictions to drive intelligent growth

See our complete guide: [Predictive AI for financial services (debt, mortgage, lending)](/blog/debt-mortgage-lending-complete-context-guide)

[Direct mail remains a core acquisition strategy for many financial services providers](https://www.lob.com/blog/the-benefits-of-direct-mail-marketing-in-todays-economy), but it comes with a hefty price tag. Each mailer represents an investment, and when sent indiscriminately, costs can spiral with little return.

When this client first came to us at the end of 2024, like many in the industry, they relied primarily on instinct-based direct mail strategies, meaning a significant portion of their outreach was reaching consumers who were unlikely to convert. They knew they were overspending but lacked the tools to pinpoint which mailers were worth sending—and which could be cut without hurting revenue.

This client needed a solution that could optimize their outreach, reduce waste, and maintain strong enrollment numbers. Most of all, they wanted confidence in whatever decision they made—all without disrupting their existing workflow. So we provided them a custom predictive data point that did exactly that.

## Lead suppression with Faraday

And that’s where Faraday came in. As soon as we identified their challenges, we quickly devised a targeted strategy to help.

The answer? Faraday’s [lead suppression](https://faraday.ai/templates/lead-suppression/recharge) template.

To enact this strategy, we started by inputting the provider’s recent lead data into our system (maintaining rigorous data privacy standards like HIPAA, GDPR, and SOC-2 compliance throughout the process). This data was then enriched through our [identity resolution](https://faraday.ai/blog/identity-resolution) process with 3rd party data from the built-in Faraday Identity Graph (FIG), which [includes over 1,400 attributes on more than 240 million U.S. consumers and their households](https://faraday.ai/features/faraday-identity-graph), and features nuanced demographic and behavioral indicators that influence consumer actions—ranging from property ownership to life events and purchasing habits.

From there, new predictive models were created and trained on this historical enrollment data (now enriched with 3rd party insights), identifying patterns and trends that distinguished high-value leads from low-value ones. Then, using advanced machine learning techniques, the system assigned each lead a predictive score, ranking them into ten distinct deciles based on their likelihood to convert. With this scoring system in place, a [cut-off threshold](https://medium.com/swlh/determining-a-cut-off-or-threshold-when-working-with-a-binary-dependent-target-variable-7c2342cf2a7c) was established to determine the point at which the cost of printing and sending a mailer outweighed the expected return, ensuring that outreach efforts remained cost-effective.

Finally, Faraday delivered an optimized, high-fit mailing list back to the provider and armed with this refined list, they were able to move forward with confidence, ensuring that every mailer sent had a greater likelihood of driving enrollment. For a visual breakdown of this process, check out our recent blog on lead suppression, [here](https://faraday.ai/blog/holdout-testing-with-faraday).

So how did it go?

## The results (so far)

If you noted the timestamp on this article, you might be thinking to yourself, “Well, they reached out at the end of 2024 and this was published on Feb 21, 2025, so did they even get the models rolled out yet?”

Well, despite the short timeframe, they not only deployed the models and sent direct mail based on their predictions, but have also already seen a significant reduction in costs, all while maintaining strong engagement with high-quality leads.

Indeed, by suppressing low-likelihood recipients, they were already able to send over **80,000 fewer mailers**, producing **a net savings of nearly $40,000**, demonstrating the immediate financial benefit of a more targeted approach.

These early financial wins were certainly affirming, but it had only been a month. How did we know that they were actually the result of our model and not just an uptick consumer interest this year?

### Ongoing model validation

At Faraday, we take the [validation](https://faraday.ai/docs/reference/getscopeefficacy) of our models very seriously. We do this before predictions are deployed through our [holdout testing process](https://faraday.ai/blog/holdout-testing-with-faraday) and continue testing on an ongoing basis even after successful implementation. In this case even though we were already seeing positive results, we wanted to see data that confirmed (or challenged) our assumptions. So we conducted further analysis on our models after we started getting results back to confirm that our predictive scoring was actually aligning with real-world outcomes.

And we found what we were looking for: Leads with the lowest predictive scores showed below-average conversion rates, validating the suppression strategy. Meanwhile, leads in the highest-scoring segments consistently outperformed the average, reinforcing the value of prioritizing the right audience. And what’s even better is that this correlation is only expected to strengthen as more data flows through the model, further refining predictions and maximizing efficiency.

While these financial results would be impressive on their own, they also highlight how quickly Faraday’s models can be deployed and optimized. The contract was signed in late-December, and within weeks, the provider was already seeing meaningful returns. This quick turn around, combined with the ability to continuously refine and improve targeting over time, positions them for even greater efficiency and cost savings in the months and years ahead.

## Next steps

With direct mail suppression already demonstrating clear value, the provider is now looking to further optimize their customer outreach efforts. A key next step? [Call center list scoring](https://faraday.ai/stories/call-center).

Similar to how Faraday’s predictive models helped refine direct mail targeting, they can also help [prioritize leads](https://faraday.ai/templates/lead-prioritization) for call center campaigns. By focusing call center agents’ efforts on high-likelihood prospects, the provider can reduce wasted calls, improve conversion rates, and increase overall campaign efficiency.

We can’t wait to see how they use this technology to generate even more value for their business\!

## In conclusion

In just a short time, this financial services provider has revolutionized its direct mail strategy, reducing unnecessary costs while maintaining strong engagement with high-value leads. Faraday’s predictive modeling has enabled them to move beyond broad, instinct-driven targeting to a data-backed approach, ensuring each mailer sent delivers greater value. And with tens of thousands of dollars saved in only a few weeks, the provider has already realized the immediate financial benefits of a more targeted outreach strategy.

What’s more, looking forward, the provider is well-positioned to refine their targeting approach even further. By continuously updating and optimizing their predictive models, and exploring additional use cases like call center list scoring, they can maximize the efficiency of all customer acquisition channels.

For this company, this is just the beginning of a smarter way to decide who’s worth reaching—and when.

If you'd like to know how Faraday can produce similar results for your business, **sign up for free**!

In their first month with Faraday, this financial services provider used lead suppression to cut 80,000 low-value mailers and save nearly $40,000.

How this financial services provider optimized their direct mail outreach in just one month

_This customer story has been anonymized to respect the company's competitive strategy._

## The problem: optimizing their lead scoring methodology

A national home services brand was trying to optimize their lead-to-customer conversion rate. Their existing lead handling methodology was to engage all leads upon entry into their funnel via a call center, then an SMS and email engagement drip for those leads that did not convert. Their active lead engagement window was 19 days from funnel entry to lead conversion. Leads were scored using a rules-based methodology of first-in, first-out with identity verification completed upon form fill completion. Beyond this, all leads were treated the same.

This longer sales cycle resulted in sales representatives having more leads than they could consistently follow up with. The company believed this dynamic was negatively impacting conversion rates with sales reps spending too much time on prospects with a low chance of converting into customers, and too little time on those who were a great fit for their services.

## The obstacle: limitations of rules based scoring models

Conventional B2C lead scoring models commonly utilize a rules-based approach, where human judgment determines the rules for scoring leads. Typically, this involves calculating a lead score by establishing rules from a blend of basic demographic, property, or identity details (e.g. owner-occupied household, identity verified, etc.) and observed behaviors such as website visits, email interactions, form completions, and/or call length.

Although these rule-based models aid marketing and sales teams in prioritizing their efforts, they carry notable limitations.

1. The criteria for lead scoring in a rules based approach are often subjectively chosen, leading to inaccuracies in assessing a lead's actual potential.
2. There is a cold start problem for a new lead that has exhibited little to no behavior. A new lead that has just entered the sales funnel often can't be scored, or the score is inaccurate due to a lack of observed behavior.
3. Rules reliant on demographic, property, and/or identity criteria often involve either having to ask the lead for this information, which impacts conversion rates, or licensing 3rd party data, which can be costly and increase a company's privacy compliance and liability burdens.

As a result of these limitations, this national home services team started exploring alternatives to traditional lead scoring.

## Why Faraday?

### Quantitative rather than subjective lead scoring

Unlike a rule-based lead scoring approach, [predictive lead scoring](/blog/predictive-lead-scoring-b2c) uses machine learning to analyze historical lead data and create statistical models. This approach predicts the likelihood of a lead becoming a customer through quantitative analysis, rather than qualitative assessment.

Faraday's predictive modeling eliminates subjectivity from lead scoring by incorporating both intuitive and non-intuitive data points that traditional lead scoring might miss. The model updates continuously with new customer data, ensuring that changes in the market or product offerings are reflected in the score. Unlike rule-based scoring, which can become outdated, predictive scoring always evaluates leads based on the most current and relevant information.

Additionally, Faraday's [out-of-the-box identity graph](/features/faraday-identity-graph) eliminates the need to collect identity criteria or rely on expensive data vendors. With just an email or physical address, Faraday can instantly access over 1,400 data points on nearly every adult consumer in the country, both providing a deep well of data to find signals and eliminating the cold start problem of how to score leads with very little data.

## The results

### 4x higher lead conversion

The marketing and analytics team at this nationwide home improvement brand integrated their lead data into the Faraday platform. The data used for training these models included multiple years of lead, appointment, and customer records. The Faraday platform automatically associated these records with thousands of demographic, property, financial, and lifestyle attributes found in the Faraday Identity Graph.

From there, the brand quickly built predictive lead scoring models to assess and rank leads based on their likelihood to convert into paying customers. The models found objective patterns across the client and Faraday's data that clearly distinguished a good lead from a bad lead.

To validate the accuracy and effectiveness of Faraday's lead scores, the home improvement brand engaged the agency Bold Orange to conduct a thorough analysis of the efficacy of the predictive lead scores, comparing Faraday model predictions against actual lead conversions and outcomes.

[![Image of a third party holdout analysis](/images/stories/screenshots/bold-orange-analysis.png)](/images/stories/screenshots/bold-orange-analysis.png)

The analysis revealed that the conversion rate of leads in the top 10% of lead scores was **4x higher than the brand's baseline conversion rate**, confirming the model's ability to accurately identify high-potential leads.

With this third-party verification in place, the brand deployed the models into production. The models scored the brand's leads in real-time the moment they entered their sales funnel and used the scores to prioritize leads within their call center. Now, the sales team has the confidence to know that they are talking to the best opportunity at any given moment.

A third-party analysis revealed that the conversion rate of leads in the top 10% of lead scores was 4x higher than the brand's baseline conversion rate, confirming Faraday's ability to accurately identify high-potential leads.

How Faraday's predictive lead scoring helps a national brand identify leads with a 4X conversion rate

**See our complete guide:** [Powered by Faraday — embedding predictive AI in your platform](/blog/powered-by-faraday-complete-guide-embedded-predictive-ai).

---

[![DataGrid header image](/images/stories/screenshots/salesrabbit-story-header-image.png)](/images/stories/screenshots/salesrabbit-story-header-image.png)

## Making a powerful tool even smarter

SalesRabbit is the leader in software for field sales teams. Their [DataGrid](https://salesrabbit.com/datagrid/) solution supplies canvassers with household data on specific properties and homeowners in their territories, providing essential context as they knock on doors. However, the SalesRabbit product team recognized that static data points on each property provided limited context for sales reps.

They realized that, if they could identify which attributes of a homeowner or property could predict the likelihood that a prospect would become a customer, then they could use that information to inform how their sales reps approached their day-to-day responsibilities. For example, solar clients would not only know which houses had the right amount of sun exposure for solar panels, but also which of them most closely fit the model of historical solar adopters. Pest control clients would not only know what material houses were made out of, they could also know if the homeowner would be more or less likely to say yes to regularly scheduled inspections or not, and so on.

Furthermore, if they had a way to predict which neighborhoods had the highest concentration of likely customers, then DataGrid could direct sales reps' efforts more efficiently.

## The challenges of building predictive AI models from scratch

SalesRabbit identified three main obstacles to them building effective predictive AI models in-house:

- A lack of data about consumers.
- The long ramp time to build effective predictive models.
- Resources to test models for accuracy and continue to provide transparency about the models efficacy.

### Licensing more customer data

Predictive models need a deep well of data to find an accurate signal. Despite the wealth of data SalesRabbit had on homeowners, adding an ML layer to DataGrid would require thousands more data points to ensure accurate models. Psychographic data, shopping behaviors, demographic data, financial information, and more would all need to be vetted, licensed, and reconciled with their existing data before predictions could be made.

### Extensive time and resources dedicated to prediction

To build a customer prediction pipeline in-house, the SalesRabbit team would also need to grow their existing data science team. They couldn't just reorganize other team members for a temporary sprint. Not only is the typical timeline for an ML program multiple years, they’d need to invest to continuously maintain those models over time.

### Limited transparency and scalability of cost

For the C-level team at SalesRabbit, it was critical for the solution to their challenge to be scalable. The original timeline for building this feature entirely in-house was multiple years and required a major investment from their data science team to get started.

Additionally, SalesRabbit's customer base includes both enterprise home service companies and small, regional installers. Creating a customer prediction pipeline comes with a tremendous computing cost, and running hundreds of millions of scenarios to build a model that works quickly can grow that cost exponentially–and unpredictably. How do you forecast the budget for the computing cost when the number of predictions made monthly for large and small organizations varies dramatically from month to month?

## The partnership

[![DataGrid mock](/images/stories/screenshots/datagrid-mock.png)](/images/stories/screenshots/datagrid-mock.png)

Realizing that an in-house build carried these risks, the SalesRabbit team shopped for existing best-in-class infrastructure that they could build on.

Faraday was the perfect fit for the team to implement customer predictions quickly, and at scale.

### Built-in consumer data for predicting customer behavior

Faraday has over [1,400+ attributes](/features/faraday-identity-graph) built into its platform, which mean all of this data was available on day one. Even better, it could be seamlessly combined with their existing data sets–no identity resolution required.

Employing this font of data on nearly every U.S. adult, SalesRabbit had everything they needed to understand the patterns and trends of their customers' ideal homeowners, regardless of which vertical their customers work in.

### Preserving existing data science resources and cutting product build time with Faraday recipes

Faraday’s plug-and-play [customer behavior predictions](/features/customer-behavior-predictions) meant there was no need to pull their data scientists off of other projects to build a customer prediction pipeline from scratch. SalesRabbit used pre-built prediction recipes as an accelerator to test and refine models before incorporating them into their product. This allowed them to shrink a multi-year roadmap to a matter of days, and free up their data science team to focus on other efforts.

### Peace of mind with transparency and scalability of cost

To the product team at SalesRabbit, building a customer prediction pipeline from scratch came with no small level of forecasting uncertainty. How much should they invest to launch? How much will it cost to scale up as their customer base grows?

With Faraday, they found pricing certainty, which allowed SalesRabbit to know exactly what to expect as they scale up.

## The results

[![DataGrid UI screenshot](/images/stories/screenshots/datagrid.jpg)](/images/stories/screenshots/datagrid.jpg)

To SalesRabbit, choosing to embed customer predictions into their software rather than build it in-house was a game-changer. Once SalesRabbit connected their data and created their initial predictive models, Faraday’s developer API made it easy to start embedding Faraday’s predictive features into DataGrid, and in a fraction of their original timeline.

Launched as “DataGrid AI,” SalesRabbit’s predictive lead scoring tool guides canvassing teams to best-fit territories, helps with route planning, and leaves reps with clear guidance on which households to prioritize when in the field. And because all of Faraday’s predictions are cached in advance, overall app performance improved once they were integrated.

Launched as “DataGrid AI,” SalesRabbit’s predictive lead scoring tool guides canvassing teams to best-fit territories, helps with route planning, and leaves reps with clear guidance on which households to prioritize when in the field.

How SalesRabbit uses Faraday to enhance their field sales app with data & AI

See our complete guide: [Powered by Faraday — embedding predictive AI in your platform](/blog/powered-by-faraday-complete-guide-embedded-predictive-ai)

_This customer story has been anonymized to respect the company’s competitive strategy._

A leading CRM used by roofers leveraged Faraday’s infrastructure to power their Lead Intelligence tool, which helps their customers see which leads are likely to convert so that no time is wasted on bad fits.

## Giving clients an edge

The product team at this CRM for roofers is focused on helping users find value in their product, and in the five years before adopting [customer predictions](/features/predictions), they observed a 10% drop in their user base’s conversion rate. After investigating why, the product team found that, as the customer's lead generation tools grew more advanced and varied, the lead data quality was reduced, and it became more difficult for their users to see what was valuable to them. With this in mind, they determined that they needed better ways for users to cut through the noise and surface their most valuable leads–and they settled on a solution in AI [lead scoring](/solutions/lead_management).

> “We wanted to be able to help our customers’ sales teams close more deals faster. Faraday’s lead scoring and data could help us do just that.” - **VP of Product at a leading CRM for roofers**

## Finding a solution and time-to-market that fits

Although the product team unearthed an excellent opportunity to help their customers, building AI lead scoring entirely in-house was out of the question, as they didn’t have the resources to do so, and they knew that time was of the essence, so the long timeline wasn’t going to work for them. They also knew they needed purpose-built infrastructure to scale up as they grew.

With Faraday, this roofing software company found an infrastructure designed to predict lead and customer behavior and comes with a [consumer identity graph](/features/data) that includes data on nearly every U.S. adult. This–plus Faraday’s 10+ years of experience–gave their product team the confidence to build and launch a successful product in their desired timeline.

> “If we’d had the resources to build this pipeline ourselves fully, we were looking at a time-to-market of over a year. We integrated with Faraday in about three months, and it’s been worth it for that alone–not to mention the data and expertise we’ve received.” **VP of Product at a leading CRM for roofers**

## Making predictions actionable

The roofing CRM’s software engineers embedded Faraday’s lead scores into their product and provided a way to filter lead results by lead rank. With lead rank, every lead and prospect in the roofing software is assigned a rank based on how likely they are to convert, which is displayed as the lead rank status. Before partnering with Faraday to surface this likelihood, The roofing CRM’s users might spend days nurturing a sales opportunity only to have it fall flat because there wasn’t a good fit.

Their users can easily filter by the highest lead rank, which perfectly fits the ultimate goal at the start–to surface actionable insights that help focus roofing sales teams.

[![Image of roofing CRM user interface](/images/blog/leading-roofing-crm-lead-scoring/roofing-crm-ui.png)](/images/blog/leading-roofing-crm-lead-scoring/roofing-crm-ui.png)

In the first six months since launch, the roofing CRM’s clients who have turned on the new feature close deals 10% faster than those who haven’t–netting an additional $3k+ per month for your average contractor. The product team at the CRM for roofers is thrilled with the results so far and are keen to leverage more of Faraday’s infrastructure to build additional features, focusing on [explainability](/blog/what-is-explainable-ai-why-is-it-important) to give users a clear sense of why an individual lead is receiving its score.

[Create a free Faraday account](https://faraday.ai/signup) today.

A leading CRM used by roofers powers their Lead Intelligence tool with predictive lead scores, which helps their customers see which leads are most likely to convert.

How a leading CRM used by roofers employs Faraday to help clients close deals 10% faster

**See our complete guide:** [Predictive AI for financial services (debt, mortgage, lending)](/blog/debt-mortgage-lending-complete-context-guide).

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A leading **debt consolidation** company relied on direct mail as a key channel for acquiring new customers. However, a significant portion of their marketing budget was being spent on sending mail to leads who were unlikely to convert. This inefficiency not only increased costs but also diluted the impact of their outreach efforts, making it difficult to connect with high-intent buyers.

## Smarter targeting with Faraday's lead suppression

To address this challenge, the company partnered with Faraday to implement a [predictive lead suppression model](https://faraday.ai/templates/lead-suppression/recharge). This process began with Faraday ingressing their lead data to assess the key characteristics of their existing customer base. Then, through careful analysis of their historical conversion patterns—as well as the inclusion of custom predictive data points, built using [rich 3rd party data and attributes on 240M+ US consumers](https://faraday.ai/features/faraday-identity-graph) that comes preloaded through the [Faraday Identity Graph (FIG)](https://faraday.ai/docs/abstractions/traits)—we were able to predict which of their leads were most likely to convert.

Using this analysis, Faraday’s model identified the bottom 10%—the least-likely-to-convert leads—and suppressed them from the company’s direct mail campaigns. This approach immediately reduced wasted marketing spend and allowed the company to reallocate resources toward higher-value prospects.

## Or see Faraday's lead suppression process broken down visually

1. ![Faraday Identity Graph](/images/blog/lead-supp-panel-0.png)

   ### Don’t start from scratch

   Faraday comes preloaded with 1400 attributes on 240M+ US consumers and their households.

   This powerful dataset provides high-signal data points—including property data, demographics, life events, and buying habits—that can shape smarter decisions.

2. ![client data](/images/blog/lead-supp-panel-1.png)

   ### Your lead data is shared with Faraday

   Your lead data is collected and ingressed into Faraday securely.

   This guarantee is achieved through our robust data security policies, which include HIPAA, GDPR, and SOC-2 compliance.

3. ![model is trained and predictions begin](/images/blog/lead-supp-panel-2.png)

   ### We train predictive model by analyzing client data for patterns

   Faraday’s Identity Graph & AI/ML predictive modeling enables us to identify unique trends in your data set and specifically assess your leads’ likelihood to convert.

4. ![leads are sorted into deciles](/images/blog/lead-supp-panel-3.png)

   ### We identify how likely each lead is to convert

   We identify 10 deciles to be able to help you determine which leads are the best (most likely to convert after receiving mail) and the worst (those that will cost more in postage/print than they will generate in value, or simply won’t convert).

5. ![cut-off threshold is established](/images/blog/lead-supp-panel-4.png)

   ### We establish a cut-off threshold

   Faraday then helps you to calculate the “cut-off” threshold. This determines where the cost to print/send the mailer is greater than the likelihood to convert to revenue.

6. ![targeted client list](/images/blog/lead-supp-panel-5.png)

   ### We return the targeted list

   Once Faraday has removed the low-fit leads, you can use the new list to target your direct mail process and proceed with your next mail drop.

## Optimized results, supported by data scientists

Although this model was already delivering significant value, our data science team wasn’t satisfied.

That was because this company recognized four distinct customer segments within their broader population—each with unique characteristics. Although the one-size-fits-all approach had made sense initially to drive results, we hypothesized that a single model that handled all leads at once was likely not actually able to provide the highest level of precision possible. Instead, we considered creating four additional models which would be individually tailored to each unique customer segment.

While we were confident that this more granular approach would improve results, at Faraday, we prioritize empiricism over intuition so we decided to run rigorous validation tests. And indeed, after comparing conversion trends between the original model alone and all five models together, we were able to prove—rather than assume—the added value of the more granular approach. For the client, this meant even better precision in their targeting and more return on their direct mail spend.

While this process required a deeper investment of our data science team’s time, the outcome was clear: greater accuracy, better optimization, and ultimately, higher value. At Faraday, this isn’t just a one-off added service—it’s how we operate. Strategic data science investment is the only way to unlock insights at this level, and this project stands as a testament to that philosophy.

## The result

The impact of Faraday’s predictive lead suppression was immediate and substantial:

- **At least 600,000 fewer pieces of wasted direct mail** sent to low-intent leads monthly
- **Over $100,000 saved each month** in direct mail costs
- **An additional \$20,000 in monthly revenue, or $240,000 annually**, thanks to these targeted models’ better precision in predicting which customers would convert

With these enhancements, the company is now seeing a **10x return on investment (ROI)** from Faraday’s predictive modeling.

## Beyond cost savings: data-driven growth

By investing in Faraday’s predictive modeling and data science expertise, the company didn’t just cut costs—they redefined the way they acquire customers. Suppressing low-intent leads was just the beginning. By refining their approach with segment-specific models, they turned direct mail into a high-precision growth engine, ensuring that every dollar spent reached the right audience.

With this new strategy, the company isn’t just saving money—it’s driving measurable, sustained growth. Faraday’s predictive lead suppression has become a core part of their marketing playbook, proving that the right data science investment doesn’t just optimize campaigns—it transforms businesses.

Through predictive lead suppression, this company was able to cut at least 600,000 pieces of least-likely-to-convert direct mail each month.

Customer stories